Sampling Adaptive Learning Algorithm for Mobile Blind Source Separation

Jingwen Huang,Jianshan Sun

doi:10.1155/2018/5048419

Abstract

Learning rate plays an important role in separating a set of mixed signals through the training of an unmixing matrix, to recover an approximation of the source signals in blind source separation (BSS). To improve the algorithm in speed and exactness, a sampling adaptive learning algorithm is proposed to calculate the adaptive learning rate in a sampling way. The connection for the sampled optimal points is described through a smoothing equation. The simulation result shows that the performance of the proposed algorithm has similar Mean Square Error (MSE) to that of adaptive learning algorithm but is less time consuming.

Highlights

With the fast development of the information and computation technologies, the big data analysis and cognitive computing have been widely used in many research areas such as medical treatment [1], transportation [2], and wireless communication [3, 4]
With the fast development of mobile computing, Blind Source Separation (BSS) has been widely used in the mobile signal analysis
Artificial neural network based Independent Component Analysis (ICA) is the widely used method in BSS, because it provides powerful tools to capture the structure in data by learning

Summary

Introduction

With the fast development of the information and computation technologies, the big data analysis and cognitive computing have been widely used in many research areas such as medical treatment [1], transportation [2], and wireless communication [3, 4]. Artificial neural network based Independent Component Analysis (ICA) is the widely used method in BSS, because it provides powerful tools to capture the structure in data by learning Based on this theory, Natural Gradient Algorithm (NGA) is employed to find the appropriate coefficient vector of artificial neural network [10]. Von Hoff and Lindgren [15] developed adaptive step size control algorithm for gradient-based BSS They used the coefficients of the estimating function to provide an appropriate “measure of error” and serve as the basis for a selfadjusting time-varying step-size. A conjugate gradient procedure with the step size derived optimally at each iteration was proposed to solve the optimization problem In these algorithms, the step size is updated in iteration, whose value is adjusted according to the time-varying dynamics of the signals. The comparison between the adaptive learning algorithm and the sampling adaptive learning algorithm is made, illustrating that the proposed algorithm has similar Mean Square Error (MSE) to that of the adaptive learning algorithm but consumes less computational time

Blind Signal Separation

Sampling Adaptive Learning Algorithm

Experiment and Result

Conclusions